Lead ML Devops Engineer

What you'll do

• Design, build, and maintain scalable MLOps frameworks on Google Cloud Platform
• Automate deployment, testing, monitoring, and lifecycle management of machine learning models
• Establish repeatable and standardized ML deployment processes across environments
• Implement model versioning, artifact management, and deployment governance standards
• Support model retraining, rollback, and release management processes
• Deploy Python-based machine learning models into production environments
• Build automated deployment pipelines for batch and real-time inference workloads
• Develop reusable deployment templates and automation frameworks
• Support model serving using Vertex AI Endpoints and containerized deployment architectures
• Ensure high availability, reliability, and scalability of production ML services
• Design and implement CI/CD pipelines for machine learning applications and services
• Integrate source control, testing, and deployment workflows into enterprise delivery pipelines
• Implement Infrastructure-as-Code (IaC) practices for repeatable environment provisioning
• Support environment management across development, testing, and production environments
• Design and support cloud-native ML infrastructure on GCP
• Manage and optimize services including Vertex AI, Cloud Storage, BigQuery, Cloud Build, Cloud Run, Kubernetes Engine (GKE), Pub/Sub
• Optimize infrastructure for performance, reliability, security, and cost efficiency
• Troubleshoot production issues and support platform stability initiatives
• Implement monitoring and alerting frameworks for deployed machine learning services
• Track model performance, operational health, latency, and system utilization
• Support model lifecycle governance and operational compliance requirements
• Establish logging, observability, and operational dashboards
• Drive best practices for production support and operational excellence

Requirements

• Bachelor's degree in Computer Science, Engineering, Information Technology, or related discipline
• 5 - 8 years of experience in Cloud Engineering, MLOps, or ML Platform Engineering
• Strong hands-on experience with Google Cloud Platform (GCP)
• Proven experience deploying and operationalizing Python-based machine learning models
• Strong experience with Vertex AI and production ML deployment patterns
• Experience building CI/CD pipelines for machine learning applications
• Experience implementing Infrastructure-as-Code using Terraform or similar tools
• Experience monitoring and supporting production machine learning workloads
• Strong troubleshooting and problem-solving skills
• Preferred: Google Cloud Professional Machine Learning Engineer Certification
• Preferred: Familiarity with MLflow, Kubeflow, or similar MLOps frameworks

Tech stack